Artificial intelligence (AI) in medicine as a strategic valuable tool

Medical, dental, and nursing students' attitudes and knowledge towards artificial intelligence: a systematic review and meta-analysis

BACKGROUND

Nowadays, Artificial intelligence (AI) is one of the most popular topics that can be integrated into healthcare activities. Currently, AI is used in specialized fields such as radiology, pathology, and ophthalmology. Despite the advantages of AI, the fear of human labor being replaced by this technology makes some students reluctant to choose specific fields. This meta-analysis aims to investigate the knowledge and attitude of medical, dental, and nursing students and experts in this field about AI and its application.

METHOD

This study was designed based on PRISMA guidelines. PubMed, Scopus, and Google Scholar databases were searched with relevant keywords. After study selection according to inclusion criteria, data of knowledge and attitude were extracted for meta-analysis.

RESULT

Twenty-two studies included 8491 participants were included in this meta-analysis. The pooled analysis revealed a proportion of 0.44 (95%CI = [0.34, 0.54], P < 0.01, I2 = 98.95%) for knowledge. Moreover, the proportion of attitude was 0.65 (95%CI = [0.55, 0.75], P < 0.01, I2 = 99.47%). The studies did not show any publication bias with a symmetrical funnel plot.

CONCLUSION

Average levels of knowledge indicate the necessity of including relevant educational programs in the student's academic curriculum. The positive attitude of students promises the acceptance of AI technology. However, dealing with ethics education in AI and the aspects of human-AI cooperation are discussed. Future longitudinal studies could follow students to provide more data to guide how AI can be incorporated into education.

Gelatin Methacryloyl (GelMA)-Based Biomaterial Inks: Process Science for 3D/4D Printing and Current Status

Tissue engineering for injured tissue replacement and regeneration has been a subject of investigation over the last 30 years, and there has been considerable interest in using additive manufacturing to achieve these goals. Despite such efforts, many key questions remain unanswered, particularly in the area of biomaterial selection for these applications as well as quantitative understanding of the process science. The strategic utilization of biological macromolecules provides a versatile approach to meet diverse requirements in 3D printing, such as printability, buildability, and biocompatibility. These molecules play a pivotal role in both physical and chemical cross-linking processes throughout the biofabrication, contributing significantly to the overall success of the 3D printing process. Among the several bioprintable materials, gelatin methacryloyl (GelMA) has been widely utilized for diverse tissue engineering applications, with some degree of success. In this context, this review will discuss the key bioengineering approaches to identify the gelation and cross-linking strategies that are appropriate to control the rheology, printability, and buildability of biomaterial inks. This review will focus on the GelMA as the structural (scaffold) biomaterial for different tissues and as a potential carrier vehicle for the transport of living cells as well as their maintenance and viability in the physiological system. Recognizing the importance of printability toward shape fidelity and biophysical properties, a major focus in this review has been to discuss the qualitative and quantitative impact of the key factors, including microrheological, viscoelastic, gelation, shear thinning properties of biomaterial inks, and printing parameters, in particular, reference to 3D extrusion printing of GelMA-based biomaterial inks. Specifically, we emphasize the different possibilities to regulate mechanical, swelling, biodegradation, and cellular functionalities of GelMA-based bio(material) inks, by hybridization techniques, including different synthetic and natural biopolymers, inorganic nanofillers, and microcarriers. At the close, the potential possibility of the integration of experimental data sets and artificial intelligence/machine learning approaches is emphasized to predict the printability, shape fidelity, or biophysical properties of GelMA bio(material) inks for clinically relevant tissues.

Artificial Intelligence: Knowledge and Attitude Among Lebanese Medical Students

Background Artificial intelligence (AI) has taken on a variety of functions in the medical field, and research has proven that it can address complicated issues in various applications. It is unknown whether Lebanese medical students and residents have a detailed understanding of this concept, and little is known about their attitudes toward AI. Aim This study fills a critical gap by revealing the knowledge and attitude of Lebanese medical students toward AI. Methods A multi-centric survey targeting 365 medical students from seven medical schools across Lebanon was conducted to assess their knowledge of and attitudes toward AI in medicine. The survey consists of five sections: the first part includes socio-demographic variables, while the second comprises the 'Medical Artificial Intelligence Readiness Scale' for medical students. The third part focuses on attitudes toward AI in medicine, the fourth assesses understanding of deep learning, and the fifth targets considerations of radiology as a specialization. Results There is a notable awareness of AI among students who are eager to learn about it. Despite this interest, there exists a gap in knowledge regarding deep learning, albeit alongside a positive attitude towards it. Students who are more open to embracing AI technology tend to have a better understanding of AI concepts (p=0.001). Additionally, a higher percentage of students from Mount Lebanon (71.6%) showed an inclination towards using AI compared to Beirut (63.2%) (p=0.03). Noteworthy are the Lebanese University and Saint Joseph University, where the highest proportions of students are willing to integrate AI into the medical field (79.4% and 76.7%, respectively; p=0.001). Conclusion It was concluded that most Lebanese medical students might not necessarily comprehend the core technological ideas of AI and deep learning. This lack of understanding was evident from the substantial amount of misinformation among the students. Consequently, there appears to be a significant demand for the inclusion of AI technologies in Lebanese medical school courses.

Mapping the Bibliometrics Landscape of AI in Medicine: Methodological Study

BACKGROUND

Artificial intelligence (AI), conceived in the 1950s, has permeated numerous industries, intensifying in tandem with advancements in computing power. Despite the widespread adoption of AI, its integration into medicine trails other sectors. However, medical AI research has experienced substantial growth, attracting considerable attention from researchers and practitioners.

OBJECTIVE

In the absence of an existing framework, this study aims to outline the current landscape of medical AI research and provide insights into its future developments by examining all AI-related studies within PubMed over the past 2 decades. We also propose potential data acquisition and analysis methods, developed using Python (version 3.11) and to be executed in Spyder IDE (version 5.4.3), for future analogous research.

METHODS

Our dual-pronged approach involved (1) retrieving publication metadata related to AI from PubMed (spanning 2000-2022) via Python, including titles, abstracts, authors, journals, country, and publishing years, followed by keyword frequency analysis and (2) classifying relevant topics using latent Dirichlet allocation, an unsupervised machine learning approach, and defining the research scope of AI in medicine. In the absence of a universal medical AI taxonomy, we used an AI dictionary based on the European Commission Joint Research Centre AI Watch report, which emphasizes 8 domains: reasoning, planning, learning, perception, communication, integration and interaction, service, and AI ethics and philosophy.

RESULTS

From 2000 to 2022, a comprehensive analysis of 307,701 AI-related publications from PubMed highlighted a 36-fold increase. The United States emerged as a clear frontrunner, producing 68,502 of these articles. Despite its substantial contribution in terms of volume, China lagged in terms of citation impact. Diving into specific AI domains, as the Joint Research Centre AI Watch report categorized, the learning domain emerged dominant. Our classification analysis meticulously traced the nuanced research trajectories across each domain, revealing the multifaceted and evolving nature of AI's application in the realm of medicine.

CONCLUSIONS

The research topics have evolved as the volume of AI studies increases annually. Machine learning remains central to medical AI research, with deep learning expected to maintain its fundamental role. Empowered by predictive algorithms, pattern recognition, and imaging analysis capabilities, the future of AI research in medicine is anticipated to concentrate on medical diagnosis, robotic intervention, and disease management. Our topic modeling outcomes provide a clear insight into the focus of AI research in medicine over the past decades and lay the groundwork for predicting future directions. The domains that have attracted considerable research attention, primarily the learning domain, will continue to shape the trajectory of AI in medicine. Given the observed growing interest, the domain of AI ethics and philosophy also stands out as a prospective area of increased focus.

Evolution of Diagnostic and Forensic Microbiology in the Era of Artificial Intelligence

Diagnostic microbiology plays a vital role in managing infectious diseases, combating antimicrobial resistance, and containment of outbreaks. During the fourth industrial revolution, when artificial intelligence (AI) became an essential part of our day-to-day lives, its integration into healthcare would further revolutionize our knowledge and potential. Although in the budding stage, AI with machine learning is being increasingly utilized in various aspects of diagnostic microbiology. It can handle large datasets that are difficult to analyze manually. Researchers have developed and demonstrated several machine-learning algorithms for interpreting bacterial cultures, conducting image analysis for microbial detection, and predicting antimicrobial susceptibility patterns. Thus, AI may most likely be the ultimate solution to the ever-increasing demand for improved results with shorter turnaround times. AI can also assist forensic microbiologists in crime scene investigations, as it can guide individual identification, cause and time since death, and manner of death. This review summarizes the application of AI in diagnostic microbiology for performing diverse sets of microbial investigations and is an essential aid in forensic microbiology.

Multi-Omics and Management of Follicular Carcinoma of the Thyroid

Follicular thyroid carcinoma (FTC) is the second most common cancer of the thyroid gland, accounting for up to 20% of all primary malignant tumors in iodine-replete areas. The diagnostic work-up, staging, risk stratification, management, and follow-up strategies in patients who have FTC are modeled after those of papillary thyroid carcinoma (PTC), even though FTC is more aggressive. FTC has a greater propensity for haematogenous metastasis than PTC. Furthermore, FTC is a phenotypically and genotypically heterogeneous disease. The diagnosis and identification of markers of an aggressive FTC depend on the expertise and thoroughness of pathologists during histopathological analysis. An untreated or metastatic FTC is likely to de-differentiate and become poorly differentiated or undifferentiated and resistant to standard treatment. While thyroid lobectomy is adequate for the treatment of selected patients who have low-risk FTC, it is not advisable for patients whose tumor is larger than 4 cm in diameter or has extensive extra-thyroidal extension. Lobectomy is also not adequate for tumors that have aggressive mutations. Although the prognosis for over 80% of PTC and FTC is good, nearly 20% of the tumors behave aggressively. The introduction of radiomics, pathomics, genomics, transcriptomics, metabolomics, and liquid biopsy have led to improvements in the understanding of tumorigenesis, progression, treatment response, and prognostication of thyroid cancer. The article reviews the challenges that are encountered during the diagnostic work-up, staging, risk stratification, management, and follow-up of patients who have FTC. How the application of multi-omics can strengthen decision-making during the management of follicular carcinoma is also discussed.

Automatic reconstruction of interstitial needles using CT images in post-operative cervical cancer brachytherapy based on deep learning

Purpose

The purpose of this study was to investigate the precision of deep learning (DL)-based auto-reconstruction in localizing interstitial needles in post-operative cervical cancer brachytherapy (BT) using three-dimensional (3D) computed tomography (CT) images.

Material and methods

A convolutional neural network (CNN) was developed and presented for automatic reconstruction of interstitial needles. Data of 70 post-operative cervical cancer patients who received CT-based BT were used to train and test this DL model. All patients were treated with three metallic needles. Dice similarity coefficient (DSC), 95% Hausdorff distance (95% HD), and Jaccard coefficient (JC) were applied to evaluate the geometric accuracy of auto-reconstruction for each needle. Dose-volume indexes (DVI) between manual and automatic methods were used to analyze the dosimetric difference. Correlation between geometric metrics and dosimetric difference was evaluated using Spearman correlation analysis.

Results

The mean DSC values of DL-based model were 0.88, 0.89, and 0.90 for three metallic needles. Wilcoxon signed-rank test indicated no significant dosimetric differences in all BT planning structures between manual and automatic reconstruction methods (p > 0.05). Spearman correlation analysis demonstrated weak link between geometric metrics and dosimetry differences.

Conclusions

DL-based reconstruction method can be used to precisely localize the interstitial needles in 3D-CT images. The proposed automatic approach could improve the consistency of treatment planning for post-operative cervical cancer brachytherapy.

Using a machine learning-based risk prediction model to analyze the coronary artery calcification score and predict coronary heart disease and risk assessment

OBJECTIVES

To calculate the coronary artery calcification score (CACS) obtained from coronary artery computed tomography angiography (CCTA) examination and combine it with the influencing factors of coronary artery calcification (CAC), which is then analyzed by machine learning (ML) to predict the probability of coronary heart disease(CHD).

METHODS

All patients who were admitted to the Affiliated Hospital of Traditional Chinese Medicine of Southwest Medical University from January 2019 to March 2022, suspected of CHD, and underwent CCTA inspection were retrospectively selected. The degree of CAC was quantified based on the Agatston score. To compare the correlation between the CACS and clinical-related factors, we collected 31 variables, including hypertension, diabetes, smoking, hyperlipidemia, among others. ML models containing the random forest (RF), radial basis function neural network (RBFNN),support vector machine (SVM),K-Nearest Neighbor algorithm (KNN) and kernel ridge regression (KRR) were used to assess the risk of CHD based on CACS and clinical-related factors.

RESULTS

Among the five ML models, RF achieves the best performance about accuracy (ACC) (78.96%), sensitivity (SN) (93.86%), specificity(Spe) (51.13%), and Matthew's correlation coefficient (MCC) (0.5192).It also has the best area under the receiver operator characteristic curve (ROC) (0.8375), which is far superior to the other four ML models.

CONCLUSION

Computer ML model analysis confirmed the importance of CACS in predicting the occurrence of CHD, especially the outstanding RF model, making it another advancement of the ML model in the field of medical analysis.

Orbital and eyelid diseases: The next breakthrough in artificial intelligence?

Orbital and eyelid disorders affect normal visual functions and facial appearance, and precise oculoplastic and reconstructive surgeries are crucial. Artificial intelligence (AI) network models exhibit a remarkable ability to analyze large sets of medical images to locate lesions. Currently, AI-based technology can automatically diagnose and grade orbital and eyelid diseases, such as thyroid-associated ophthalmopathy (TAO), as well as measure eyelid morphological parameters based on external ocular photographs to assist surgical strategies. The various types of imaging data for orbital and eyelid diseases provide a large amount of training data for network models, which might be the next breakthrough in AI-related research. This paper retrospectively summarizes different imaging data aspects addressed in AI-related research on orbital and eyelid diseases, and discusses the advantages and limitations of this research field.

Gastroenterology in the Metaverse: The dawn of a new era?

2021 is known as the first Year of the Metaverse, and around the world, internet giants are eager to devote themselves to it. In this review, we will introduce the concept, current development, and application of the Metaverse and the use of the current basic technologies in the medical field, such as virtual reality and telemedicine. We also probe into the new model of gastroenterology in the future era of the Metaverse.